Wipro Big Data Engineer Interview Questions, Process, and Tips

Q1. Mostly about spark topics like shuffeling joins, data skew, and pyspark scenarios
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Q2. Mostly about python and sql scenarios
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Q1. About behavioural questions
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Q2. Day to day work and workflow model based questions
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Q1. Why you need to join infosys
Ans. 
I want to join Infosys because of its reputation for innovation and growth opportunities.
• Infosys is known for its cutting-edge technology solutions and innovative projects.

• I am impressed by Infosys' commitment to employee development and career growth.

• I believe that joining Infosys will provide me with the opportunity to work on challenging projects and enhance my skills.
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Q2. Self introduction
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Questions on software and system designs

Helps employer identify particular personality traits like leadership, confidence, interpersonal and teamwork skills of potential employees

Q1. Why do you want this job? Again, companies want to hire people who are passionate about the job, so you should have a great answer about why you want the ...
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Q2. This opportunity can help me in many ways to boost my confidence as well as drive my career forward
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Easy only so prepare well that's it

Q1. What method you use
Ans. 
I use a combination of programming languages, tools, and frameworks to analyze and process large datasets.
• Utilize programming languages like Python, Java, or Scala for data processing

• Leverage tools like Hadoop, Spark, or Kafka for distributed computing

• Implement frameworks like MapReduce or Apache Flink for data analysis

• Use SQL or NoSQL databases for data storage and retrieval
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Q2. Why you lleave current company
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Q3. What skill you have
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Q4. Tell about ur project
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Q5. What you implemented
Ans. 
Implemented a real-time data processing system using Apache Kafka and Spark for analyzing customer behavior.
• Developed data pipelines to ingest, process, and analyze large volumes of data

• Utilized Apache Kafka for real-time data streaming

• Implemented machine learning algorithms for predictive analytics

• Optimized data storage and retrieval for faster query performance
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Q1. Coalesce vs repartition
Ans. 
Coalesce is used to reduce the number of partitions in a DataFrame, while repartition is used to increase the number of partitions.
• Coalesce is a narrow transformation that can only decrease the number of partitions without shuffling data.

• Repartition is a wide transformation that can both increase or decrease the number of partitions and involves shuffling data across the cluster.

• Coalesce is more efficient for reducing ...read more
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Rank vs dense rank quetions ctes

Python data structure

Q1. What is speculative execution in Hadoop?
Ans. 
Speculative execution in Hadoop is a feature that allows the framework to launch duplicate tasks for a job, with the goal of completing the job faster.
• Speculative execution is used when a task is taking longer to complete than expected.

• Hadoop identifies slow-running tasks and launches duplicate tasks on other nodes.

• The first task to complete is used, while the others are killed to avoid duplication of results.

• This help...read more
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Q2. Spark Architecture
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Q3. Difference in list , tuple,sets
Ans. 
Lists are mutable ordered collections, tuples are immutable ordered collections, and sets are mutable unordered collections.
• Lists are mutable and ordered, allowing for duplicate elements. Example: [1, 2, 3, 3]

• Tuples are immutable and ordered, allowing for duplicate elements. Example: (1, 2, 3, 3)

• Sets are mutable and unordered, not allowing for duplicate elements. Example: {1, 2, 3}
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Q4. Difference in rank, dense rank in sql
Ans. 
Rank assigns a unique rank to each distinct row, while dense rank assigns consecutive ranks to rows with the same values.
• Rank function assigns unique ranks to each distinct row in the result set

• Dense rank function assigns consecutive ranks to rows with the same values

• Rank function leaves gaps in the ranking sequence if there are ties, while dense rank does not
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Q5. Add data into a partitioned hive table
Ans. 
To add data into a partitioned hive table, you can use the INSERT INTO statement with the PARTITION clause.
• Use INSERT INTO statement to add data into the table.

• Specify the partition column values using the PARTITION clause.

• Example: INSERT INTO table_name PARTITION (partition_column=value) VALUES (data);
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WAP to add index wise elements of a list . A=[1,2,3] , B=[4,5,7] C should be [5,7,10]

Q1. Scenario based questions
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Q1. Project explanation , internal vs external table
Ans. 
Internal tables are managed by Hive, while external tables are managed by the user.
• Internal tables are stored in a Hive-managed warehouse directory, while external tables can be stored anywhere.

• Internal tables are deleted when the table is dropped, while external tables are not.

• External tables can be used to access data stored in non-Hive formats, such as CSV or JSON.

• Internal tables are typically used for temporary or ...read more
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Q1. What is your current salary and your expectations
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